Bring Orders into Uncertainty: Enabling Efficient Uncertain Graph Processing via Novel Path Sampling on Multi-Accelerator Systems

Uncertain or probabilistic graphs have been ubiquitously used to represent noisy, incomplete, and inaccurate linked data in many emerging big-data mining and analytics applications. It is impractical to solve uncertain graph problems exactly as it requires to evaluate an exponential number of certain instances (or "possible worlds") generated from an uncertain graph. Previously, several CPU-based techniques were proposed to use sampling for uncertain graph processing. However, we observe that (1) they suffer from low computation efficiency and large memory overhead due to unnecessary edge sampling at runtime; (2) they cannot leverage the massive parallelism provided by modern general-purpose accelerators; and (3) there lacks a general programming framework for high-performance uncertain graph processing. To tackle these challenges, we propose a novel runtime path sampling method, which is able to identify and eliminate unnecessary edge sampling via incremental path identification and filtering, resulting in significant reduction in computation and data movement. Centered around this idea, we introduce a general uncertain graph processing framework for multi-GPU systems, named BPGraph(1). BPGraph provides general support for users to design and optimize a wide-range of uncertain graph algorithms and applications without concerning about the underlying complexity. Extensive evaluation on a variety of real-world uncertain graph applications demonstrates an average speedup of 26x (up to 43x) and better scalability from BPGraph over the state-of-the-art frameworks.